• Data science is an emerging field that combines important concepts from statistics, computer science, and substantive areas of focus.

Drew Conway’s Venn diagram of data science

• Data science is the science of extracting meaning and information from data to change some outcome

• Making effective policies requires data and data evaluation

  • This is evidence-based policymaking

• Regulation will likely require knowledge of data science tools and methods

• Easier questions:

1. What is spending on healthcare like in the United States?
2. How have voting patterns for each party changed in recent years?

• Harder questions:

1. How do seat belt laws affect traffic fatalities? How can we reduce fatalities?
2. Do job training programs reduce unemployment?

Data Processing and Cleaning

• R, Python, SQL

Exploratory Data Analysis

• R, Python

Data Analysis

• Statistical Models, Machine Learning Algorithms

• R is a programming language created by statisticians
• Specifically designed for data analysis/statistics and data visualization
• It’s free! Download it here: https://www.r-project.org

• Python is a programming language created by computer scientists

• It is a general purpose language that can also be used for data analysis

• It’s free! Download it here: https://www.python.org

• SQL is a language for managing data bases

• How would you deal with 20 terabytes of data that gets updated every hour?

  • Yeah, that’s why you need SQL

• It has different dialectics, or flavors (MySQL, Oracle, Postgres

• It’s free! Download one dialect (MySQL) here: https://mysql-com.en.softonic.com/download

Let’s say you need to write a 20 page report. How will you write it?

IDEs offer better tools to help you write code!

• → RStudio
• → PyCharm, Jupyter, RStudio
• SQL → MySQL Workbench, Oracle SQL Developer, RStudio

In this workshop series, we’ll take on an example project and walk through the data science process of:

1. Data cleaning

2. Exploratory analysis

3. Modeling

4. Reporting Results

If you give an honest effort to solve each problem in this class, I promise that you will be able to do the follow at the end of the workshop:

1. Prepare a clean data for analysis

2. Identify patterns and visualize data

3. Use statistical methods to test hypotheses

4. Report your results in a reproducible manor

• R, Python, and RStudio